Enrico Capobianco

Learn More
The formulation of network models from global protein studies is essential to understand the functioning of organisms. Network models of the proteome enable the application of Complex Network Analysis, a quantitative framework to investigate large complex networks using techniques from graph theory, statistical physics, dynamical systems and other fields.(More)
Typical high-abundant proteins, including albumin, IgG, IgA and others, are the target of depletion methods usually applied to two-dimensional electrophoresis (2DE) of human biological fluids like serum and plasma. Detection of low-abundant proteins is of interest with regard to biomarkers for disease when being studied by 2DE or liquid chromatography-mass(More)
BACKGROUND The integration of protein-protein interaction networks derived from high-throughput screening approaches and complementary sources is a key topic in systems biology. Although integration of protein interaction data is conventionally performed, the effects of this procedure on the result of network analyses has not been examined yet. In(More)
do we need " networks " in systems medi-cine? Maybe there is not a simple answer. Until recently, scientists from mathematics, physics, statistics, machine learning, computer science, and similar quantitative/ computational disciplines were converging to bioinformatics for known reasons. A main one was to support the experimental and theoretical work of(More)
emphasizes the role of systems biology in medical/clinical applications. With the advent of new technologies, the " omics " explosion (i.e., next generation sequencing) and the induced changes from data-poor to data-rich applications (for instance related to high-content imaging, physiology , and structural biology) have established the necessity of a(More)
Many studies and applications in the post-genomic era have been devoted to analyze complex biological systems by computational inference methods. We propose to apply manifold learning methods to protein-protein interaction networks (PPIN). Despite their popularity in data-intensive applications, these methods have received limited attention in the context(More)